Abstract
The paper deals with the problem of time series classification for the feeding state of calves by means of features evaluated for acceleration real-time data sets. The eartags equipped with an active sensor were developed for location and animal activity identification. Video records synchronized with a sensor data were collected from three calves. After the data preprocessing including the reconstruction of lost information, filtering and frequency stabilization, new time series were used to develop a machine-learning algorithm with equidistant and non-equidistant time series segmentation method based on a modified Kolmogorov-Smirnov statistic. The proposed classification method has achieved a good recognition quality for the feeding state with a best overall accuracy of approximately 94%. Thus this methodology is useful in identifying the feeding state and we may expect the possibility to generalize it to the multi-state case as well. The further improvement of the algorithm is a subject of our future research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alvarenga, F.A.P., Borges, I., Palkovic, L., Rodina, J., Oddy, V.H., Dobos, R.C.: Using a three-axis accelerometer to identify and classify sheep behaviour at pasture. Appl. Anim. Behav. Sci. 181, 91–99 (2016)
Brodsky, B.E., Darkhovsky, B.S.: Nonparametric Methods in Change-Point Problems. Kluwer Academic Publishers, The Netherlands (1993)
Kuankid, S., Rattanawong, T., Aurasopon, A.: Classification of the cattle’s behaviours by using accelerometer data with simple behavioural technique. In: Proceedings of 2014 APSIPA Annual Summit and Conference, Siem Reap, Cambodia, pp. 1368–1372 (2014)
Marais, J., Le Roux, S.P., Wolhuter, R., Niesler, T.: Automatic classification of sheep behaviour using 3-axis accelerometer data. Livestock Sci. 196, 42–48 (2017)
Robert, B., White, B.J., Renter, D.G., Larson, R.L.: Evaluation of three-dimensional accelerometers to monitor and classify behaviour patterns in cattle. Comput. Electron. Agric. 67, 80–84 (2009)
Martiskainen, P., Järvinen, M., Skön, J.O., Tiirikainen, J., Kolehmainen, M., Mononen, J.: Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Appl. Anim. Behav. Sci. 119, 32–38 (2009)
Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis. The University of Waikato (1999)
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)
Acknowledgements
This work was funded by the Austrian Research Promotion Agency (FFG), Project No. 848610 and Smartbow GmbH. The publication was financially supported by the Ministry of Education and Science of the Russian Federation (the Agreement number 02.a03.21.0008).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sturm, V. et al. (2017). Automated Classification of a Calf’s Feeding State Based on Data Collected by Active Sensors with 3D-Accelerometer. In: Vishnevskiy, V., Samouylov, K., Kozyrev, D. (eds) Distributed Computer and Communication Networks. DCCN 2017. Communications in Computer and Information Science, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-66836-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-66836-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66835-2
Online ISBN: 978-3-319-66836-9
eBook Packages: Computer ScienceComputer Science (R0)